scholarly journals RECOGNIZING FACES USING INDIVIDUAL EIGEN SUBSPACES TECHNIQUE WITH FUZZY RULES METHODOLOGY

2015 ◽  
pp. 164-169
Author(s):  
AISHWARYA P ◽  
KARNAN MARCUS
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Rejection Rate ◽  
Security Systems ◽  
Major Objective ◽  
Face Representation ◽  
Face Images ◽  
The Face ◽  
Small Set ◽  
Effective Analysis

This paper proposes a new methodology of recognizing face using Individual Eigen Subspaces and it’s implemented in the field of Image Processing for Personnel verification or recognition. A major objective of this work is to develop a tool for face recognition, which can help in quicker and effective analysis of a face from the face set, thus reducing false acceptance rate and false rejection rate. Face recognition has been widely explored in the past years. A lot of techniques have been applied in various applications. Robustness and reliability have become more and more important for these applications especially in security systems. In this thesis, a variety of approaches for face recognition are reviewed first. These approaches are classified according to three basic tasks: face representation, face detection, and face identification. An implementation of the appearance-based face recognition method, the eigenface recognition approach, is reported. This method utilizes the idea of the principal component analysis and decomposes face images into a small set of characteristic feature images called eigenfaces. This proposed work is intended to develop, multiple face Eigen subspaces. With each one is corresponding to one known subject privately, rather than all individuals sharing one universal subspace as in the traditional eigenface method. Compared with the traditional single subspace face representation, the proposed method captures the extra personal difference to the most possible extent, which is crucial to distinguish between individuals, and on the other hand, it throws away the most intrapersonal difference and noise in the input.

Face Recognition by PCA and Improved LBP Fusion Algorithm

2015 ◽  
Vol 734 ◽  
pp. 562-567 ◽  
Author(s):  
En Zeng Dong ◽  
Yan Hong Fu ◽  
Ji Gang Tong
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Principal Component ◽  
Texture Features ◽  
Rotation Invariant ◽  
Fusion Algorithm ◽  
Face Images ◽  
The Face ◽  
Pca Method

This paper proposed a theoretically efficient approach for face recognition based on principal component analysis (PCA) and rotation invariant uniform local binary pattern texture features in order to weaken the effects of varying illumination conditions and facial expressions. Firstly, the rotation invariant uniform LBP operator was adopted to extract the local texture feature of the face images. Then PCA method was used to reduce the dimensionality of the extracted feature and get the eigenfaces. Finally, the nearest distance classification was used to distinguish each face. The method has been accessed on Yale and ATR-Jaffe face databases. Results demonstrate that the proposed method is superior to standard PCA and its recognition rate is higher than the traditional PCA. And the proposed algorithm has strong robustness against the illumination changes, pose, rotation and expressions.

An improved DFA based kernel ensemble learning machine using local feature representations for face recognition

2021 ◽  
pp. 1-14
Author(s):  
N Kavitha ◽  
K Ruba Soundar ◽  
T Sathis Kumar
Keyword(s):  
Face Recognition ◽  
Ensemble Learning ◽  
Information Gain ◽  
Recognition Task ◽  
Principal Component ◽  
Input Image ◽  
Kernel Principal Component Analysis ◽  
Face Images ◽  
The Face ◽  
Learning Machine

In recent years, the Face recognition task has been an active research area in computer vision and biometrics. Many feature extraction and classification algorithms are proposed to perform face recognition. However, the former usually suffer from the wide variations in face images, while the latter usually discard the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the Key points Local Binary/Tetra Pattern (KP-LTrP) and Improved Hough Transform (IHT) with the Improved DragonFly Algorithm-Kernel Ensemble Learning Machine (IDFA-KELM) is proposed to address the face recognition problem in unconstrained conditions. Initially, the face images are collected from the publicly available dataset. Then noises in the input image are removed by performing preprocessing using Adaptive Kuwahara filter (AKF). After preprocessing, the face from the preprocessed image is detected using the Tree-Structured Part Model (TSPM) structure. Then, features, such as KP-LTrP, and IHT are extracted from the detected face and the extracted feature is reduced using the Information gain based Kernel Principal Component Analysis (IG-KPCA) algorithm. Then, finally, these reduced features are inputted to IDFA-KELM for performing FR. The outcomes of the proposed method are examined and contrasted with the other existing techniques to confirm that the proposed IDFA-KELM detects human faces efficiently from the input images.

scholarly journals Half-face based recognition using principal component analysis

2021 ◽  
Vol 22 (3) ◽  
pp. 1404
Author(s):  
Ahmed M. Alkababji ◽  
Sara Raed Abd
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Method ◽  
Face Images ◽  
The Face ◽  
Considerable Problem ◽  
Full Face ◽  
Pca Algorithm

<span lang="EN-US">Face recognition is a considerable problem in the field of image processing. It is used daily in various applications from personal cameras to forensic investigations. Most of the provides solutions proposed based on full-face images, are slow to compute and need more storage. In this paper, we propose an effective way to reduce the features and size of the database in the face recognition method and thus we get an increase in the speed of discrimination by using half of the face. Taking advantage of face symmetry, the first step is to divide the face image into two halves, then the left half is processed using the principal component analysis (PCA) algorithm, and the results are compared by using Euclidian distance to distinguish the person. The system was trained and tested on ORL database. It was found that the accuracy of the system reached up to 96%, and the database was minimized by 46% and the running time was decreased from 120 msec to 70 msec with a 41.6% reduction.</span>

scholarly journals Face Recognition Using Eigen Face Based Technique Utilizing the Concept of Principal Component Analysis

2013 ◽  
Vol 10 (8) ◽  
pp. 1943-1953
Author(s):  
Prof. Samir K Bandyopadhyay ◽  
Mrs. Sunita Roy
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Computation Time ◽  
Principal Component ◽  
Component Analysis ◽  
Research Area ◽  
Real Time System ◽  
Face Images ◽  
Small Set ◽  
Active Research

Face recognition has been an active research area since late 1980s [1]. Eigenface approach is one of the earliest appearance-based face recognition methods, which was developed by M. Turk and A. Pentland [1] in 1991. In this approach we have to perform a lots of computations, which are not feasible with respect to time in many real time system. The concept of principal component analysis (PCA) is used in this approach to reduce the dimension and hence reducing the computation time. Principal component analysis [4] decomposes face images into a small set of characteristic feature images called eigen faces.

SUBSPACE-BASED FACE RECOGNITION: OUTLIER DETECTION AND A NEW DISTANCE CRITERION

2005 ◽  
Vol 19 (04) ◽  
pp. 479-493 ◽  
Author(s):  
PEI CHEN ◽  
DAVID SUTER
Keyword(s):  
Face Recognition ◽  
Outlier Detection ◽  
Principal Component ◽  
Dimensional Subspace ◽  
Recognition Algorithm ◽  
New Techniques ◽  
Face Images ◽  
The Face ◽  
Low Dimensional ◽  
New Strategies

Illumination effects, including shadows and varying lighting, make the problem of face recognition challenging. Experimental and theoretical results show that the face images under different illumination conditions approximately lie in a low-dimensional subspace, hence principal component analysis (PCA) or low-dimensional subspace techniques have been used. Following this spirit, we propose new techniques for the face recognition problem, including an outlier detection strategy (mainly for those points not following the Lambertian reflectance model), and a new error criterion for the recognition algorithm. Experiments using the Yale-B face database show the effectiveness of the new strategies.

scholarly journals Implementation of Facial Recognition for Home Security Systems

2018 ◽  
Vol 7 (4.10) ◽  
pp. 55
Author(s):  
Arnab Pushilal ◽  
Sulakshana Chakraborty ◽  
Raunak Singhania ◽  
P. Mahalakshmi
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Facial Recognition ◽  
Principal Component ◽  
Training Data ◽  
Lower Dimension ◽  
Security Measures ◽  
Security Systems ◽  
Home Security ◽  
The Face

In this paper, the design and development of a home security system has been detailed which uses facial recognition to conform the identity of the visitor and taking various security measures when an unauthorized personnel tries accessing the door. It demonstrates the implementation of one of the most popular algorithm for face recognition i.e. principal component analysis for the purpose of security door access. Since PCA converts the images into a lower dimension without losing on the important features, a huge set of training data can be taken. If the face is recognized as known then the door will open otherwise it will be categorized as unknown and the microcontroller (Arduino Uno) will command the buzzer to start ringing.  

scholarly journals Face recognition using eigenface approach

2012 ◽  
Vol 9 (1) ◽  
pp. 121-130 ◽  
Author(s):  
Marijeta Slavkovic ◽  
Dubravka Jevtic
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Experimental Results ◽  
Face Images ◽  
Face Recognition System ◽  
Small Set ◽  
Pca Method ◽  
Pca Algorithm

In this article, a face recognition system using the Principal Component Analysis (PCA) algorithm was implemented. The algorithm is based on an eigenfaces approach which represents a PCA method in which a small set of significant features are used to describe the variation between face images. Experimental results for different numbers of eigenfaces are shown to verify the viability of the proposed method.

scholarly journals Long distance face recognition for enhanced performance of internet of things service interface

2014 ◽  
Vol 11 (3) ◽  
pp. 961-974 ◽  
Author(s):  
Hae-Min Moon ◽  
Sung Pan
Keyword(s):  
Face Recognition ◽  
Distance Measure ◽  
Human Life ◽  
Recognition Algorithm ◽  
Security Systems ◽  
Long Distance ◽  
Training Images ◽  
Face Images ◽  
Euclidean Distance Measure ◽  
The Face

As many objects in the human ambient environment are intellectualized and networked, research on IoT technology have increased to improve the quality of human life. This paper suggests an LDA-based long distance face recognition algorithm to enhance the intelligent IoT interface. While the existing face recognition algorithm uses single distance image as training images, the proposed algorithm uses face images at distance extracted from 1m to 5m as training images. In the proposed LDA-based long distance face recognition algorithm, the bilinear interpolation is used to normalize the size of the face image and a Euclidean Distance measure is used for the similarity measure. As a result, the proposed face recognition algorithm is improved in its performance by 6.1% at short distance and 31.0% at long distance, so it is expected to be applicable for USN?s robot and surveillance security systems.

scholarly journals Recognition of Reconstructed Frontal Face Images Using FFT-PCA/SVD Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Louis Asiedu ◽  
Felix O. Mettle ◽  
Joseph A. Mensah
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Environmental Constraints ◽  
Biometric Data ◽  
Face Images ◽  
The Face ◽  
Left And Right ◽  
Value Decomposition ◽  
User Friendly

Face recognition has gained prominence among the various biometric-based methods (such as fingerprint and iris) due to its noninvasive characteristics. Modern face recognition modules/algorithms have been successful in many application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). In spite of these achievements, the performance of current face recognition algorithms/modules is still inhibited by varying environmental constraints such as occlusions, expressions, varying poses, illumination, and ageing. This study assessed the performance of Principal Component Analysis with singular value decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing the face recognition algorithm on left and right reconstructed face images. The study found that average recognition rates for the FFT-PCA/SVD algorithm were 95% and 90% when the left and right reconstructed face images are used as test images, respectively. The result of the paired sample t-test revealed that the average recognition distances for the left and right reconstructed face images are not significantly different when FFT-PCA/SVD is used for recognition. FFT-PCA/SVD is recommended as a viable algorithm for recognition of left and right reconstructed face images.

scholarly journals Face Recognition on Bag Locking Mechanism

2019 ◽  
Vol 18 (2) ◽  
pp. 16-22
Author(s):  
Mohamad Hafis Izran Ishak ◽  
Nurul Hawani Idris ◽  
Shafishuhaza Sahlan
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Raspberry Pi ◽  
Theory Approach ◽  
Human Beings ◽  
Face Images ◽  
Locking Mechanism ◽  
Small Set ◽  
Pca Method ◽  
Analyse Data

With the emergent of biometric technology, people are no longer afraid to keep their important things in the safe box or room or even facility. This is because; human beings have unique features that distinguish them with other people. The scheme is based on an information theory approach that decomposes face images into a small set of characteristic feature images called ‘Eigenfaces’, which are actually the principal components of the initial training set of face images. In this report, thorough explanation on design process of face recognition on bags locking mechanism will be elucidated. The results and analysis of the proposed design prototype also presented and explained. The platform for executing the algorithm is on the Raspberry Pi. There are two artificial intelligent techniques applied to manipulate and processing data which is fuzzy logic and neural networks. Both systems are interdependent with each other, so that it can calculate and analyse data precisely. The receive image from the camera is analysed through the Eigenfaces algorithm. The algorithm is using Principal Component Analysis (PCA) method which comprise of artificial neural network paradigm and also statistical paradigm.

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